Comparative transcriptomics method to infer gene coexpression networks and its applications to maize and rice leaf transcriptomes

Time-series transcriptomes of a biological process obtained under different conditions are useful for identifying the regulators of the process and their regulatory networks. However, such data are 3D (gene expression, time, and condition), and there is currently no method that can deal with their f...

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Published in:	Proceedings of the National Academy of Sciences - PNAS Vol. 116; no. 8; pp. 3091 - 3099
Main Authors:	Chang, Yao-Ming, Lin, Hsin-Hung, Liu, Wen-Yu, Yu, Chun-Ping, Chen, Hsiang-June, Wartini, Putu Puja, Kao, Yi-Ying, Wu, Yeh-Hua, Lin, Jinn-Jy, Lu, Mei-Yeh Jade, Tu, Shih-Long, Wu, Shu-Hsing, Shiu, Shin-Han, Ku, Maurice S. B., Li, Wen-Hsiung
Format:	Journal Article
Language:	English
Published:	United States National Academy of Sciences 19-02-2019
Series:	PNAS Plus
Subjects:	Anatomy Biological activity Biological Sciences Corn Darkness Gene expression Gene Expression Regulation, Plant - genetics Gene Regulatory Networks - genetics Genes Leaves Networks Oryza Oryza - genetics Photoperiod Photosynthesis Photosynthesis - genetics Plant Leaves - genetics Plant Proteins PNAS Plus Regulators Ribulose-bisphosphate carboxylase Ribulose-Bisphosphate Carboxylase - genetics Time series Transcriptome - genetics Zea mays Zea mays - genetics gene coexpression C4 enzymes comparative transcriptomics Kranz anatomy
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Summary:	Time-series transcriptomes of a biological process obtained under different conditions are useful for identifying the regulators of the process and their regulatory networks. However, such data are 3D (gene expression, time, and condition), and there is currently no method that can deal with their full complexity. Here, we developed a method that avoids time-point alignment and normalization between conditions. We applied it to analyze time-series transcriptomes of developing maize leaves under light–dark cycles and under total darkness and obtained eight time-ordered gene coexpression networks (TO-GCNs), which can be used to predict upstream regulators of any genes in the GCNs. One of the eight TO-GCNs is light-independent and likely includes all genes involved in the development of Kranz anatomy, which is a structure crucial for the high efficiency of photosynthesis in C₄ plants. Using this TO-GCN, we predicted and experimentally validated a regulatory cascade upstream of SHORTROOT1, a key Kranz anatomy regulator. Moreover, we applied the method to compare transcriptomes from maize and rice leaf segments and identified regulators of maize C₄ enzyme genes and RUBISCO SMALL SUBUNIT2. Our study provides not only a powerful method but also novel insights into the regulatory networks underlying Kranz anatomy development and C₄ photosynthesis.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Contributed by Wen-Hsiung Li, December 17, 2018 (sent for review October 16, 2018; reviewed by Kousuke Hanada and Nicholas J. Provart) Author contributions: Y.-M.C., H.-H.L., W.-Y.L., C.-P.Y., S.-L.T., S.-H.W., S.-H.S., M.S.B.K., and W.-H.L. designed research; Y.-M.C., H.-H.L., W.-Y.L., C.-P.Y., H.-J.C., P.P.W., Y.-Y.K., Y.-H.W., J.-J.L., M.-Y.J.L., M.S.B.K., and W.-H.L. performed research; Y.-M.C., W.-Y.L., C.-P.Y., and J.-J.L. analyzed data; and Y.-M.C., H.-H.L., W.-Y.L., C.-P.Y., M.-Y.J.L., S.-L.T., S.-H.W., S.-H.S., M.S.B.K., and W.-H.L. wrote the paper. 1Y.-M.C., H.-H.L., W.-Y.L., and C.-P.Y. contributed equally to this work. Reviewers: K.H., Kyushu Institute of Technology; and N.J.P., University of Toronto.
ISSN:	0027-8424 1091-6490
DOI:	10.1073/pnas.1817621116